
import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:str	,  a						:VQModel	,  a						:UNetaDModel	,  a						:DDIMScheduler		)						->					Tuple:
								super().__init__()
								self.register_modules(vqvae=a	,  unet=a	,  scheduler=a		)







	@torch.no_grad()
	def __call__(   self						:str	,  a						:int = 1	,  a						:Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  a						:float = 0.0	,  a						:int = 5_0	,  a						:Optional[str] = "pil"	,  a						:bool = True	,  **a						:Any	,  )						->					Union[Tuple, ImagePipelineOutput]:
								__UpperCamelCase      :  List[str]       =   randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,  generator=a	,  )
								__UpperCamelCase      :  Optional[Any]       =   latents.to(self.device		)

								# scale the initial noise by the standard deviation required by the scheduler
								__UpperCamelCase      :  Tuple       =   latents * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(a		)

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								__UpperCamelCase      :  Tuple       =   "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)

								__UpperCamelCase      :  Optional[Any]       =   {}
								if accepts_eta:
															__UpperCamelCase      :  List[Any]       =   eta

								for t in self.progress_bar(self.scheduler.timesteps		):
															__UpperCamelCase      :  str       =   self.scheduler.scale_model_input(a	,  a		)
															# predict the noise residual
															__UpperCamelCase      :  Tuple       =   self.unet(a	,  a		).sample
															# compute the previous noisy sample x_t -> x_t-1
															__UpperCamelCase      :  List[str]       =   self.scheduler.step(a	,  a	,  a	,  **a		).prev_sample

								# decode the image latents with the VAE
								__UpperCamelCase      :  Union[str, Any]       =   self.vqvae.decode(a		).sample

								__UpperCamelCase      :  List[str]       =   (image / 2 + 0.5).clamp(0	,  1		)
								__UpperCamelCase      :  List[str]       =   image.cpu().permute(0	,  2	,  3	,  1		).numpy()
								if output_type == "pil":
															__UpperCamelCase      :  Union[str, Any]       =   self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)

def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")